This study examines the spatial and temporal variability of seasonal and annual rainfall across Ethiopia's major river basins using gauge observations and CHIRPS data for the period 1981-2023. Ethiopia's estimated mean annual rainfall is similar to 813.8 mm, but its distribution is highly uneven. The western basins Abay, Baro, and Omo receive more than 52% of the national total, highlighting their central role in the country's hydrology. Kiremit (June-September) is the dominant rainy season, contributing about 58.2% of annual rainfall nationwide. In contrast, Belg (February-May) and Bega (October-January) rainfall amounts are especially important in southeastern basins such as Genale and Shebele-Ogaden, where agriculture and pastoral livelihoods depend heavily on seasonal rainfall outside the main rainy period. Trend analysis shows that rainfall has generally increased across Ethiopia, with about 88% of the time series exhibiting positive trends. The strongest increases are observed in the Abay and Baro basins, where annual rainfall has risen by up to 8.1 mm per year. In contrast, Belg rainfall shows declining trends in the Awash, Genale, and Shebele-Ogaden basins, and the Shebele-Ogaden basin also exhibits decreases in both Kiremit and annual rainfall. Variability analysis indicates that rainfall is most stable in the Abay and Baro basins (CV approximate to 0.09) and most variable in the Tekeze-Mereb and Denakil basins (coefficient of variation, CV >0.3). Overall, the results highlight growing basin-to-basin differences in rainfall behavior and point to the need for basin-specific water and agricultural management strategies to strengthen climate resilience.
In Antarctica, snowmelt processes determine the volume and mass of ice sheets and ice shelves. The tempo, extent, and duration of surface snowmelt are increasing under climate change. Thus, investigating snowmelt characteristics is an important way to better understand the state of ice sheets and ice shelves. However, previous to this study, Vernadsky weather station data had not been applied to research on snowmelt processes. The most common approaches to determining the amount of snow melting are the heat balance method, calculation via the melting coefficient, or via the water balance of the snow cover. The application of such approaches to the conditions of Antarctica has its own specific features, related to the geographical location and the availability of the necessary observation data. At the Vernadsky weather station, observations of snow cover characteristics and solar radiation elements have gaps. This article reports the characteristics of snow cover melting based on observations of snow depth and air temperature (at 2 m height) for the Vernadsky weather station data for the period 1997-2025. During 1997-2025 at the Vernadsky Station, the observed snow accumulation season was from March to December, and the snowmelt season lasted from December to February. The multi-annual mean date of maximum snow depth is October 31 (+/- 6 days), and the multi-annual mean maximum snow depth is 211 (+/- 9) cm. The duration of snowmelt ranged from 50 to 117 days, with a multi-annual mean of 78 (+/- 3) days. The amount of snow melting ranged from 108 to 287 cm; the multi-annual mean was 162 (+/- 8) cm. The mean intensity of snowmelt ranged from 0.96 to 3.26 cm/day, in water equivalent approximately 3.45 to 11.7 mm/day, with a multiannual mean of 7.68 (+/- 0.36) mm/day.
Understanding future temperature changes in Sudan is essential for climate-risk assessment and adaptation planning in this highly vulnerable and data-scarce region. This study evaluates robust projected changes in near-surface air temperature at both annual and seasonal scales in summer (June-September) and winter (December-February) relative to the historical baseline period (1995-2014). Projections are provided for the mid-century (2041-2060) and late-century (2081-2100) under the moderate SSP2-4.5 and high-emission SSP5-8.5 scenarios, based on a bias-corrected ensemble of CMIP6 climate models. Bias correction was applied using Equidistant Quantile Mapping (EQM) for historical simulations and Delta Quantile Mapping (DQM) for future projections to ensure consistency with observed climatology. The results indicate pronounced, scenario-dependent warm-ing across all seasons. By mid-century, annual mean temperatures are projected to rise by 1.68 degrees C under SSP2-4.5 and 2.17 degrees C under SSP5-8.5, reaching 2.68 degrees C and 4.87 degrees C, respectively, by late-century. Seasonal analysis reveals the most intense and varia-ble warming during summer, while winter exhibits more stable increases with narrower inter-model uncertainties. Spatially, northern and central Sudan are expected to experience the strongest warming, with high inter-model agreement. These findings underscore escalating heat-stress risks and highlight the urgent need for emissions mitigation and targeted adaptation strategies to support climate-resilient planning and sustainable development in Sudan.
In this study, we investigate the influence of urban morphology on pre-monsoon temperature regimes in the Doon Valley, a complex intermontane basin in the western Himalayas, using high-resolution (2 & times;2 km2) Weather Research and Forecasting (WRF) model simulations for the year 2021. The model employed a threefold nested domain (18-6-2 km) configuration with National Centers for Environmental Prediction-Final Analysis (NCEP-FNL) (0.25 degrees & times;0.25 degrees) global analyses as boundary conditions. To represent the rapidly expanding built environment of Dehradun, a simulation incorporating the Urban Canopy Model with the Building Effect Parameterization (WRF-UCM-BEP) was conducted and validated against ground-based observations from both Dehradun (urbanized valley,-640 m ASL) and Mussoorie (hill station,-2005 m ASL). The inclusion of urban canopy physics markedly improved model performance in Dehradun, reducing mean absolute error and root mean square error by 15-20% relative to the control run and increasing correlation and agreement for ambient (2 m) air temperature (r = 0.83-* 0.86; index of agreement = 0.72-* 0.78). In contrast, improvements in Mussoorie were marginal, confirming that urbanization-driven heat storage and morphology dominate thermal behavior within the valley city. The WRF-UCM-BEP simulation effectively reproduced the ambient urban heat island (UHI), with persistent nighttime warming of 1-2 degrees C over Dehradun's city core and a more realistic diurnal cycle. The model also improved representation of the surface UHI, showing stronger spatial coherence and closer agreement with MODIS 1 km land-surface temperature patterns. Vertical diagnostics revealed that WRF-UCM-BEP improved the vertical depiction of boundary-layer structure, weakening katabatic winds (-1.5 to-2 m s-1), suppressing nocturnal inversion, and capturing elevated warm layers consistent with observed mixing profiles. The results demonstrate that urban morphology exerts dominant control over both ambient and surface thermal environments in Dehradun, underscoring the importance of realistic multilayer urban-canopy parameterizations in high-resolution, nested mesoscale modeling of Himalayan cities.
The channel form and processes of erosion and transportation in a river are intimately tied to the fluvial and flood regimes which, in turn, are controlled by the regional hydro-climatic conditions. The Kaveri River serves as a lifeline for the vast population. It is, therefore, necessary to understand the fluvial and flood regime characteristics of the river. Daily, monthly mean discharge data and the annual maximum series (AMS) data were obtained from the Central Water Commission. The analyses of data indicate that the monsoon regime plays a role of considerable importance in determining the river regime conditions. Like the parent stream, the mean annual hydrographs of the tributaries are characterized by one pronounced peak. Although the Kaveri River is perennial, more than 60 to 95% of the flows are recorded in the monsoon months. This is, therefore, the period of geomorphic effectiveness. The time series plots of AMS data reflect substantial maximum interannual variability. The unit discharge of the Kaveri Basin is 0.18 m3s-1km-2, which is lower than other Indian rivers. The values of the coefficient of variation of AMS data range from 0.36 to 1.89, indicating low to high variability. All values of the coefficient of skewness (Cs) are positive and vary between 0.55 and 3.39. The positive values of Cs indicate a few very high-magnitude floods during the gauged period. The envelop curve of the Kaveri Basin falls much below the world envelop curve, revealing that the basin is not capable of producing very large magnitude floods for a given catchment area. The study of hydrographs indicates that large flows occur for 7 to 15 days. Thus, the investigation indicates that the fluvial and flood regime characteristics of the Kaveri River and its tributaries are controlled by monsoonal rainfall pattern and by the release of water from the reservoirs.
Extreme weather events have significant impacts on society, water resources, health, and agriculture. In this research, we analyze recent (1951-2020) and projected (2030-2099) trends in extreme precipitation indices within the Okpara Basin at the Nanon outlet. To achieve this, eight indices of extreme precipitation indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) were calculated using daily observations and outputs from SSP245 and SSP585 scenarios based on the AWI-CM, INM-CM, and EC-Earth3 models. The Mann-Kendall and Student's t-test methods were applied to examine trends and changes in time series of extreme indices. The findings reveal that over the historical period, consecutive wet days (CWD) and the number of wet days (R1mm) showed non-significant increasing trends. The consecutive dry days (CDD), RX5day, R95p, and R99p indices indicated non-significant downward trends. Over the projected period, the CWD and CDD indices exhibit downward or upward trends depending on the scenario and climate model: the RX1day, RX5day, R95p, R99p, R1mm, and PRCPTOT indices display upward trends for all scenarios and models. An intensification of wet conditions is therefore expected in the basin, and it is important for basin managers, planners, and decision-makers to develop strategies to prevent and properly manage possible water-related crises in the basin.
Heatwaves (HWs) have emerged as some of the most serious climate-induced hazards worldwide. This research analyzes the occurrence, characteristics, and consequences of HWs across Uzbekistan between 1980 and 2020. The study primarily aims to identify heatwave thresholds, examine related meteorological patterns, and evaluate their influence on human health and agricultural systems. Using reanalysis data from the National Center for Environmental Prediction and the National Center for Atmospheric Research (NCEP/NCAR), heatwave thresholds were established based on temperature anomalies exceeding 5 degrees C above the long-term July mean. Summer heat in Uzbekistan peaks in July; Bukhara and Khorezm are identified as the regions most affected by extreme temperatures. During the 40-year period, five HWs were documented in Bukhara and seven in Khorezm. Synoptic analysis revealed that persistent cyclonic activity dominated during these episodes, leading to stagnant and exceptionally warm atmospheric conditions. Mortality statistics from the United Nations indicate that although the overall death rate has declined since the late 1970s, the health risks associated with prolonged heat events remain substantial. Agricultural sensitivity was also evident, with increasing heat contributing to reduced crop yields and water stress, thus threatening food security. Furthermore, Coupled Model Intercomparison Project Phase 6 (CMIP6) model simulations under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios suggest that continued warming will likely heighten both the frequency and duration of HWs, posing greater risks to human well-being and agricultural resilience. These results underscore the need for enhanced early warning systems, improved weather forecasting, and climate-resilient policies in Uzbekistan. Strengthening community awareness and integrating scientific insights into policy frameworks are vital for minimizing the escalating impacts of HWs in a warming environment.
The fluctuations in the observed rainfall pattern is a great concern for the rainfed-based system in Nigeria. In Southwestern Nigeria, for example, it has become difficult to predict rainfall activities in most cities and villages in the recent times. This study is focused on the analysis of rainfall and wind-flow patterns in Southwestern Nigeria. Secondary data on rainfall and wind-flow were collected from weather stations across Ikeja, Ibadan, Abeokuta, Osogbo and Akure between the years 1971 and 2020. Data were subjected to descriptive statistics with the results presented using wind flow maps. Results revealed that the recorded mean seasonal rainfall amounts of 303 mm in Ikeja, followed by Akure (229 mm), Osogbo (178 mm), and Ibadan (169 mm), were indications of local weather patterns like the influence of sea breeze that usually results in rainfall variability. The South-westerly monsoon is moisture-laden and hence a rain-bearing wind, which blows inland from the Atlantic Ocean. The study also observed the weakness of the wind during the December, January and February (DJF) months, due to the local influence of the dry harmattan wind that blows northerly from the opposite direction. This period of dryness has been accounted for in the low rainfall amount recorded in most stations. especially in Ibadan at this time. Primarily, heat, moisture and aerodynamic indices were found to be the primary control factors of rainfall patterns in the study area. It is therefore recommended that wind flow direction is an important key element to guide decisions on human outdoor activities, travel plans and agricultural practices.
This study evaluates the performance of several bias correction techniques applied to CMIP6 precipitation simulations over Sudan for the period 1991-2014, using the high-resolution CHIRPS observational dataset as a reference. Four widely used bias correction methods: Empirical Quantile Mapping (EQM), Gamma Quantile Mapping (Gamma-QM), Local Intensity Scaling (LOCI), and the Delta Method were applied to ten CMIP6 models to assess their ability to reduce systematic biases and improve consistency with observed climatology. The raw simulations reveal pronounced seasonal biases, characterized by overestimation during the pre-monsoon season (MAM) and underestimation during the monsoon season (JJAS), whereas annual biases are moderate but exhibit notable spatial heterogeneity. Among the tested techniques, EQM and Gamma-QM consistently yield the most effective corrections, achieving median bias reductions of 94-100% across both annual and seasonal timescales, and markedly enhancing Kling-Gupta Efficiency (KGE) values. Among the evaluated models, EC-Earth3, GFDL-ESM4, and INM-CM4-8 demonstrate the best performance annually and during June-September, whereas NESM3 performs better during March-May, highlighting model-specific strengths in simulating seasonal precipitation variability. Spatial analyses further confirm that bias corrections effectively align precipitation variability with observations, with statistically significant improvements across most regions of Sudan. These findings highlight the critical role of quantile-based correction methods in producing reliable CMIP6 precipitation outputs over Sudan and establish a robust framework for assessing both model skill and bias correction performance in regions characterized by complex, seasonally varying rainfall regimes.
Tropical river basins exhibit complex hydrological dynamics and are increasingly susceptible to the impacts of climate change. However, there remains a lack of data and methodological frameworks to comprehensively assess runoff responses in these regions. This study proposes a framework for evaluating the impact of climate change on runoff in the Ba River basin. Long-term trends in temperature, rainfall, and discharge from 1981 to 2020 were analyzed. The SWAT model was applied to simulate future discharge under four climate change scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) for the periods 2021-2040, 2041-2060, 2061-2080, and 2081-2100. Results indicate that annual discharge at the An Khe station (upper basin) is projected to decline by 30.2 to 39.0%, while the Cung Son station (lower basin) is expected to experience change ranging from-4.0% to +15.6%. During the flood season, discharge is projected to decrease at the An Khe station (-6.1 to-17.3%) but increase substantially at the Cung Son station (+32.0 to +57.9%). In contrast, low-flow season discharge is projected to decline sharply at both stations by 68.0 to 85.2% at the An Khe station and 86.7 to 98.6% at the Cung Son station. The anticipated reduction in low-flow-season discharge highlights critical risks for water security in tropical basins. These findings underscore the urgent need for improved management strategies and operational frameworks to ensure sustainable water use under future climate conditions.
Climate extremes have become increasingly important in recent years, leading to renewed scientific interest. However, few studies have focused on precipitation extremes in cities in Burkina Faso, a Sahelian country in West Africa. The aim of this study is to analyze trends and to project future extreme precipitation indices in three cities in Burkina Faso. To this end, precipitation data, recorded daily, were collected from the National Meteorological Agency of Burkina Faso (NMABF) over the period 1991-2020. The stations selected were Boromo for the small town of Boromo, Saria for the medium-sized town of Koudougou, and Bobo-Dioulasso for the town of Bobo-Dioulasso. The precipitation data were used to calculate the extreme precipitation indices described by ETCCDMI (Expert Team for Climate Change Detection Monitoring and Indices) using Rclimdex. Descriptive statistics, the Mann-Kendall test, and trends from innovative models were used to analyze the extreme precipitation indices; the Holt-Winters additive model was used to analyze future projections. The study showed considerable variability and a monotonic increasing trend in extreme precipitation indices over the period 1991-2020. However, for the city of Koudougou, the trend was a non-monotonic increase. The forecast based on the Holt-Winters additive model shows considerable variability in the extreme precipitation indices, with an upward trend over the period 2020-2030. On the other hand, in the city of Koudougou, indices of precipitation duration will decrease, indicating that the city will be affected most by the frequency and intensity of extreme precipitation.
Flash floods pose a significant risk to infrastructure in Kosovo, particularly in urban and riverine areas. This research focuses on an intense river flood event that took place on January 19, 2023, in the Skenderaj catchment. The study's main goal was to establish a flash flood early warning system by combining sophisticated atmospheric modeling, hydrological evaluation, and rainfall hazard analysis. The ARW model with 2-km resolution effectively captured rainfall intensity and local flood occurrences, particularly around Skenderaj and Istog, whereas the 4-km NMM model better represented wider spatial precipitation patterns. Hydrological results demonstrated that precipitation strongly dictated river discharge and runoff dynamics, with the highest flows recorded in northern Albania. To validate and enhance forecast accuracy for flash flood warnings, datasets from the Global Flood Awareness System (GloFAS), the European Flood Awareness System (EFAS), and ERA5 reanalysis were incorporated. These resources provided essential information on antecedent conditions, such as soil moisture and snowmelt, which substantially influenced runoff and flood magnitude. The ECMWF Copernicus framework also contributed by supplying 24-hour river discharge forecasts for Kosovo's basins, aiding in timely and spatially detailed flood alerts. The Novel Thunderstorm Alert System (NOTHAS) was updated to integrate crucial hydrological variables-including surface and convective runoff, snow water equivalent, soil moisture, and slope-thereby enhancing the precision of flood warnings. This improved system enabled effective classification of flood risk zones, thus identifying areas vulnerable to flash floods and landslides. The study highlights the crucial role of high-resolution weather modeling, hydrological insights, and integrated early warning systems in enhancing flash flood prediction and mitigation efforts.
This article presents the results of an analysis of the long-term variability of average monthly and annual solar radiation in Ukraine from 2011 to 2020. The measurement data are compared with earlier periods and the standard period of 1961-1990. The statistical characteristics of changes in solar radiation are determined. The characteristics of solar radiation are compared with normative data of the National Standard of Ukraine (DSTU-NBV.1.1.-27:2010). In recent years, the annual amounts of direct radiation have increased by 18-23%. The total annual radiation in 2020 was 6% greater than in 1961-1990. Hourly and daily direct and scattered radiation were analyzed with empirical dependencies and engineering calculations. The efficiency of using the sustainable potential of solar energy depends on the climatic characteristics of the specific area or region. Inconsistencies in regulations can therefore create a problem where the best places for energy production lack public interest, infrastructure, and cost-effective consumption. Depending on the regional climatic conditions of Ukraine, the solar energy potential varies from 1400 MJ/m2 in the western regions to 1950 MJ/m2 in the eastern ones. An important characteristic of solar energy resources is the duration of sunshine. For Kyiv, the average monthly duration varies from 180 h in March to 120 h in November. The possible annual duration of sunshine varies accordingly from 4452 h (2011) to 4481 h (2020). The maximum values were observed in June (331 h) and August (334 h).
Rainfall across various climatic zones of Egypt, including arid coastal and semi-arid inland regions, exhibits significant temporal and spatial variability. Precise estimation of effective rainfall depths is essential for design engineers, hydrologists, and consultants involved in the construction of hydraulic structures such as dams, lakes, culverts, and diversions. Moreover, rainfall depth plays a crucial role in the design of urban drainage systems, small-scale irrigation projects, and broader water resource management initiatives. To address this need, an atlas of isopluvial maps for Egypt was developed using statistical methodologies and Geographic Information System (GIS) tools. This study employed short-duration rainfall data from various climatic zones of Egypt to create an empirical formula for estimating short-duration rainfall depths. Maximum annual daily rainfall data from 54 stations were analyzed to estimate short-duration rainfall values. The analytical process used Gamma distributions to determine maximum rainfall depths for various return periods and durations. The derived empirical formula and daily rainfall data were then incorporated into a GIS framework for spatial interpolation and the generation of isopluvial maps. The resulting atlas provides isopluvial maps for return periods ranging from 2 to 200 years and durations from 5 minutes to 24 hours. These maps serve as a valuable resource for decision-makers and design engineers, providing reliable rainfall estimates for specific locations or regions across Egypt. Additionally, the methodology presented in this study offers practical guidance for understanding and modeling the temporal and spatial distribution of rainfall in diverse climatic regions; its potential for improving the design of hydraulic structures is highlighted. Further validation of the atlas using independent datasets is recommended.
In this study, the hydrology of Shahpur catchment is modeled to calculate the hydrological discharge of Shahpur Dam and to establish the water balance component using the Soil and Water Assessment Tool (SWAT). Shahpur catchment is located on the Nandana River basin in Pakistan, about 45 km from Islamabad and 8 km north of Fateh Jang. The Arc SWAT 2012 version 10.5.24, which was created for Arc Map 10.5, was used to delineate the study area and its sub-components, combine the data layers, and edit the model database and SWAT CUP SUFI2 algorithm for calibration and validation.. Calibration from 2000-2004 and validation from 2006-2010 employed historic daily flow data and climatic data collected from the Shahpur Dam site office and Pakistan Meteorological Department (PMD) Islamabad. Based on literature reviews, 11 parameters with stronger influence on runoff were chosen. Nash-Sutcliffe efficiency (NSE), percent bias (PBIAS), and root-mean-square/standard deviation ratio (RSR) were used as statistical indicators. Results indicated satisfactory agreement between measured and simulated discharge values at yearly and monthly scales, demonstrating robust performance during both calibration (R2 = 0.95) and validation (R2 = 0.82) periods. The findings support the applicability of the model for effective watershed management in Shahpur based on favorable indicators and comparative outcomes.
The severity of hurricanes and cyclones in Mexico increases each year. A key area of research represents the development of a mathematical model to predict their tracks and points of impact. The IBTrACS database contains data on hurricanes and tropical cyclone tracking; it is the most comprehensive global collection of tropical cyclones. This database was developed in collaboration with all Regional Specialized Meteorological Centers of the World Meteorological Organization (WMO). Using the track, wind speed, and atmospheric pressure data for each of the hurricane and cyclone occurrences from 1851 to 2022, probabilistic type I extreme value models were applied to extreme winds and atmospheric pressure. With the help of a simple Bayesian model the probabilities were computed of a hurricane or cyclone with wind of a certain magnitude occurring at a given latitude and longitude; an event occurs when specified atmospheric pressure conditions are met. The data collected correspond to the area between west longitudes 115.5 degrees and 85 degrees and north latitudes 10 degrees and 32 degrees. This database can be managed, in the future, for the forecast of hurricane and cyclone tracks.
In Antarctica, studying the near-surface wind regime is important because its dynamics directly affect the continent's ice shelves. The nearsurface wind is also important for analyzing global and regional climate. Vernadsky Station has a fairly long observation series of nearsurface wind speed. These data are widely used to research changes, variability, and trends in the near-surface wind regime on the Antarctic Peninsula. The observation series, however, has gaps and incorrect values associated with periodical updates of measurement devices. Thus, the observation data require careful evaluation of homogeneity and stationarity. The objective of this study was to investigate the homogeneity, stationarity, and tendencies of the near-surface wind speed in the area of the Vernadsky Station based on a combined approach using several statistical and graphical methods. The methods' diverse properties support more robust estimates. Consequently, five statistical tests (standard normal Alexandersson test, Buishand test, Pettitt test, von Neumann relation, and Mann-Kendall test) and three graphical methods (chronological graph, mass curve, and residual mass curve) were employed. Most of the observation series is homogeneous and stationary, except the mean annual and February mean monthly near-surface wind speeds, which display both decreasing and increasing phases in their long-term cyclical fluctuations, which are continuing. Violation of homogeneity and stationarity results from the comparison of different phases of cyclic fluctuations (decrease and increase), which have different statistical characteristics. We show that over the past 20 years at the station, the near-surface wind speed has tended to increase in all months of the year.
